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Ada-Things: An adaptive virtual machine monitoring and migration strategy for internet of things applications

机译:ADA-Lifts:用于物联网应用的自适应虚拟机监控和迁移策略

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Internet of Things (loT) applications running on mobile devices are subject to the low storage capacity and short battery lifetime. Edge clouds (EC) provide an approach to offload computation tasks and reduce network latency for these applications. The main challenge in such ecosystems is how to efficiently monitor and allocate VM resources to realize load balancing among edge clouds. In this paper, we propose Ada-Things, an adaptive VM monitoring and live migration Strategy for loT applications in edge cloud architecture. The basic idea of Ada-Things is that the migration method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current memory dirty page rate in loT applications, Ada-Things can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations (application generality and performance imbalance) of existing VM migration methods in edge cloud. Evaluation results show, compared with traditional methods, Ada Things can significantly reduce the total migration time by 21%, the VM downtime by 38% and the amount of pages transferred by 29% in average. (C) 2018 Elsevier Inc. All rights reserved.
机译:移动设备上运行的事物(LOT)应用程序的应用互联网可能会受到较低的存储容量和短电池寿命。边缘云(EC)提供卸载计算任务的方法,并降低这些应用程序的网络延迟。此类生态系统中的主要挑战是如何有效地监控和分配VM资源以实现边缘云之间的负载平衡。在本文中,我们提出了Ada-intive,在边缘云架构中的批次应用程序的自适应VM监控和实时迁移策略。 ADA-事物的基本思想是VM的迁移方法应由其工作负载特性确定。具体地,基于当前存储器脏页率的批次应用程序的变化,ADA-astion可以自适应地选择最合适的迁移方法来复制内存页面,从而寻址现有VM迁移方法的两个限制(应用程序一般性和性能不平衡)边云。评价结果表明,与传统方法相比,ADA的东西可以显着将总迁移时间减少21%,VM停机时间为38%,平均转移29%的页数。 (c)2018 Elsevier Inc.保留所有权利。

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